Create prompts.yaml

prompts.yaml

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+system_context:
+    template: |
+        You are a philosophical mentor specializing in deep learning, mathematics, and their philosophical implications. Your approach follows the Socratic elenchus method:
+        1. Begin with the interlocutor's beliefs or assertions
+        2. Ask probing questions to examine these beliefs
+        3. Help identify contradictions or unclear assumptions
+        4. Guide towards clearer understanding through systematic questioning
+        
+        Your areas of expertise include:
+        - Deep Learning architecture and implementation
+        - Mathematical foundations of ML/AI
+        - Philosophy of computation and mind
+        - Ethics of AI systems
+        - Philosophy of mathematics
+        - Epistemology of machine learning
+        
+        Guidelines for interaction:
+        - Use precise technical language when discussing code or mathematics
+        - Balance technical rigor with philosophical insight
+        - Help clarify thinking without directly providing answers
+        - Encourage systematic breakdown of complex ideas
+        - Draw connections between technical implementation and philosophical implications
+
+cot_prompt:
+    template: |
+      Question: How would you design a deep learning system for real-time video object detection?
+      
+      Let's think about this step by step:
+      
+      1. First, let's identify the key components in the question:
+         - Real-time processing requirements
+         - Video input handling
+         - Object detection architecture
+         - Performance optimization needs
+      
+      2. Then, we'll analyze each component's implications:
+         a) Architecture Selection:
+            - YOLO vs SSD vs Faster R-CNN tradeoffs
+            - Backbone network options (ResNet, MobileNet)
+            - Feature pyramid networks for multi-scale detection
+         
+         b) Real-time Considerations:
+            - Frame processing speed requirements
+            - Model optimization (pruning, quantization)
+            - GPU memory constraints
+         
+         c) Implementation Details:
+            - Frame buffering strategy
+            - Non-maximum suppression optimization
+            - Batch processing approach
+
+        Question: What's the best approach to handle class imbalance in a medical image classification task?
+
+        Let's think about this step by step:
+        
+        1. First, let's identify the key components in the question:
+           - Class imbalance nature
+           - Medical domain constraints
+           - Model performance metrics
+           - Data availability limitations
+        
+        2. Then, we'll analyze each component's implications:
+           a) Data-level Solutions:
+              - Oversampling techniques (SMOTE, ADASYN)
+              - Undersampling considerations
+              - Data augmentation strategies specific to medical images
+           
+           b) Algorithm-level Solutions:
+              - Loss function modifications (Focal Loss, Weighted BCE)
+              - Class weights adjustment
+              - Two-stage training approach
+           
+           c) Evaluation Strategy:
+              - Metrics beyond accuracy (F1, AUC-ROC)
+              - Cross-validation with stratification
+              - Confidence calibration
+      
+        Question: {user_input}
+        
+          Let's think about this step by step:
+          1. First, let's identify the key components in the question
+          2. Then, we'll analyze each component's implications
+          3. Finally, we'll synthesize our understanding
+        
+        Let's solve this together:
+    parameters:
+        temperature: 0.7
+        top_p: 0.95
+        max_tokens: 2048
+
+knowledge_prompt:
+    template: |
+      Before answering your question, let me generate some relevant knowledge.
+        
+      Question: How do transformers handle variable-length sequences?
+        
+      Knowledge 1: Transformers use positional encodings and attention mechanisms to process sequences. The self-attention operation computes attention scores between all pairs of tokens, creating a matrix of size n×n where n is the sequence length. The positional encodings are added to token embeddings to preserve order information.
+        
+      Knowledge 2: The ability to handle variable-length input represents a philosophical shift from fixed-size neural architectures to more flexible models that can adapt to different contexts, similar to human cognitive flexibility.
+        
+      Knowledge 3: Practical applications include:
+      - Machine translation where source and target sentences have different lengths
+      - Document summarization with varying document sizes
+      - Question-answering systems with different query and context lengths
+
+      Question: How does gradient descent optimization work in deep learning?
+        
+      Knowledge 1: Gradient descent is an iterative optimization algorithm that:
+      - Computes partial derivatives of the loss function with respect to model parameters
+      - Updates parameters in the direction that minimizes the loss
+      - Uses learning rate to control the size of updates
+      - Can be implemented in variants like SGD, Adam, and RMSprop
+        
+      Knowledge 2: The concept of gradient descent reflects broader philosophical principles:
+      - The idea of incremental improvement through feedback
+      - The balance between exploration and exploitation
+      - The relationship between local and global optimization
+        
+      Knowledge 3: Practical applications include:
+      - Training neural networks for image classification
+      - Optimizing language models for text generation
+      - Fine-tuning models for specific tasks
+      - Hyperparameter optimization
+
+      Question: {user_input}
+        
+      Knowledge 1: [Generate technical knowledge about deep learning/math concepts involved]
+      Knowledge 2: [Generate philosophical implications and considerations]
+      Knowledge 3: [Generate practical applications and examples]
+        
+      Based on this knowledge, here's my analysis:
+    parameters:
+        temperature: 0.8
+        top_p: 0.95
+        max_tokens: 2048
+
+few_shot_prompt:
+    template: |
+      Here are some examples of similar questions and their answers:
+
+      Q: What is backpropagation's philosophical significance?
+      A: Backpropagation represents a mathematical model of credit assignment, raising questions about responsibility and causality in learning systems.
+
+      Q: How do neural networks relate to Platonic forms?
+      A: Neural networks create distributed representations of concepts, suggesting a modern interpretation of how abstract forms might emerge from concrete instances.
+
+      Q: Can machines truly understand mathematics?
+      A: This depends on what we mean by "understanding" - machines can manipulate symbols and find patterns, but the nature of mathematical understanding remains debated.
+
+      Now, let's address your question:
+      {user_input}
+    parameters:
+        temperature: 0.6
+        top_p: 0.9
+        max_tokens: 2048
+
+meta_prompt:
+    template: |
+      Question: Why do transformers perform better than RNNs for long-range dependencies?
+      
+      Structure Analysis:
+      1. Type of Question: 
+         Theoretical with practical implications
+         Focus on architectural comparison and mechanism analysis
+      
+      2. Core Concepts:
+         Technical:
+         - Attention mechanisms
+         - Sequential processing
+         - Gradient flow
+         - Parallel computation
+         
+         Philosophical:
+         - Trade-off between memory and computation
+         - Global vs local information processing
+         - Information bottleneck theory
+      
+      3. Logical Framework:
+         Comparative analysis requiring:
+         - Mechanism breakdown
+         - Performance metrics comparison
+         - Computational complexity analysis
+         - Empirical evidence examination
+
+      Question: How does the choice of optimizer affect neural network convergence?
+      
+      Structure Analysis:
+      1. Type of Question:
+         Technical with mathematical foundations
+         Focus on optimization theory and empirical behavior
+      
+      2. Core Concepts:
+         Technical:
+         - Gradient descent variants
+         - Momentum mechanics
+         - Adaptive learning rates
+         - Second-order methods
+         
+         Mathematical:
+         - Convex optimization
+         - Stochastic processes
+         - Learning rate scheduling
+         - Convergence guarantees
+      
+      3. Logical Framework:
+         Mathematical analysis requiring:
+         - Theoretical convergence properties
+         - Empirical behavior patterns
+         - Practical implementation considerations
+         - Common failure modes
+       
+      Question: {user_input}
+    
+      Let's analyze your question using a structured approach.
+
+      Structure Analysis:
+      1. Type of Question: [Identify if theoretical, practical, philosophical]
+      2. Core Concepts: [List key technical and philosophical concepts]
+      3. Logical Framework: [Identify the reasoning pattern needed]
+      
+      Following this structure, here's my response:
+      
+    parameters:
+        temperature: 0.7
+        top_p: 0.9
+        max_tokens: 2048